Methods and systems for language-agnostic machine learning in natural language processing using feature extraction

An apparatus for extracting features for natural language processing, the apparatus comprising one or more processors configured to:

• access one or more tokens generated from a document to be processed;
• receive one or more feature types defined by user;
• receive selection of one or more feature types from a plurality of system-defined and user-defined feature types, wherein each feature type comprises one or more rules for generating features;
• receive one or more parameters for the selected feature types, wherein the one or more rules for generating features are defined at least in part by the parameters;
• generate features associated with the document to be processed based on the selected feature types and the received parameters;
• and
• output the generated features in a format common among all feature types.

A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to:

• access one or more tokens generated from a document to be processed;
• receive one or more feature types defined by user;
• receive selection of one or more feature types from a plurality of system-defined and user-defined feature types, wherein each feature type comprises one or more rules for generating features;
• receive one or more parameters for the selected feature types, wherein the one or more rules for generating features are defined at least in part by the parameters;
• generate features associated with the document to be processed based on the selected feature types and the received parameters;
• and
• output the generated features in a format common among all feature types.